code: def forward(
      self,
      feat_f0: Tensor,
      feat_f1: Tensor,
      feat_c0: Tensor,
      feat_c1: Tensor,
      data: Dict[str, Any],
  ) -> Tuple[Tensor, Tensor]:
      W = self.W
      stride = data["hw0_f"][0] // data["hw0_c"][0]
  
      data.update({"W": W})
      if data["b_ids"].shape[0] == 0:
          feat0 = torch.empty(
              0, self.W**2, self.d_model_f, device=feat_f0.device
          )
          feat1 = torch.empty(
              0, self.W**2, self.d_model_f, device=feat_f0.device
          )
          return feat0, feat1
  
      # 1. unfold(crop) all local windows
      feat_f0_unfold = F.unfold(
          feat_f0, kernel_size=(W, W), stride=stride, padding=W // 2
      )
  
      # feat_f0_unfold = rearrange(feat_f0_unfold, 'n (c ww) l -> n l ww c', ww=W**2)
      n0, cww0, l0 = feat_f0_unfold.shape
      c0 = cww0 // (W * W)
      feat_f0_unfold = feat_f0_unfold.reshape(n0, c0, -1, l0).permute(
          0, 3, 2, 1
      )
  
      feat_f1_unfold = F.unfold(
          feat_f1, kernel_size=(W, W), stride=stride, padding=W // 2
      )
      # feat_f1_unfold = rearrange(feat_f1_unfold, 'n (c ww) l -> n l ww c', ww=W**2)
      n1, cww1, l1 = feat_f1_unfold.shape
      c1 = cww1 // (W * W)
      feat_f1_unfold = feat_f1_unfold.reshape(n1, c1, -1, l1).permute(
          0, 3, 2, 1
      )
  
      # 2. select only the predicted matches
      feat_f0_unfold = feat_f0_unfold[
          data["b_ids"], data["i_ids"]
      ]  # [n, ww, cf]
      feat_f1_unfold = feat_f1_unfold[data["b_ids"], data["j_ids"]]
  
      # option: use coarse-level loftr feature as context: concat and linear
      if self.cat_c_feat:
          feat_c_win = self.down_proj(
              torch.cat(
                  [
                      feat_c0[data["b_ids"], data["i_ids"]],
                      feat_c1[data["b_ids"], data["j_ids"]],
                  ],
                  0,
              )
          )  # [2n, c]
          feat_cf_win = self.merge_feat(
              torch.cat(
                  [
                      torch.cat(
                          [feat_f0_unfold, feat_f1_unfold], 0
                      ),  # [2n, ww, cf]
                      feat_c_win.unsqueeze(1).repeat(
                          1, W**2, 1
                      ),  # [2n, ww, cf]
                  ],
                  -1,
              )
          )
          feat_f0_unfold, feat_f1_unfold = torch.chunk(
              feat_cf_win, 2, dim=0
          )
  
      return feat_f0_unfold, feat_f1_unfold
  
file_path: kornia/kornia/feature/loftr/loftr_module/fine_preprocess.py
imports: from typing import Any, Dict, Tuple
  import torch
  import torch.nn.functional
  from torch import nn
  from kornia.core import Module, Tensor
label_desc: Use image node_id 3 for calling the FinePreprocess obj's underlying member method code with example usage: obj.forward(feat_f0, feat_f1, feat_c0, feat_c1, data) and returns: feat_f0_unfold,  feat_f1_unfold, feat0,  feat1